Bibliographic record
Abstract
Aim: Investigate the role of mobile phones in use by the international LUHS students Objectives: 1) Determine the most common practices and habits of mobile phones usage among international students, 2) Investigate consequences and possible effects of habits through comparison of results to literature of previous studies performed determining mobile phone use and well-being, 3) Determine possible consequences and effects of current habits of mobiles phone use among international students. Materials and methods: Cross-sectional study was performed at the Lithuanian University of Health Sciences during the first quarter of 2022. Total population of international students present in LUHS consists of 1300 full-time students; sample size reached 52 student participants. Research was done applying Smartphone addiction scale, shortened version, and a previously tested form for determination of mobile phone use. The form was presented to participants as an online link that directed them to the documents which they completed anonymously. Consent was taken from each student prior to participation; data of student responses were further analyzed with descriptive statistical analysis, exploratory analysis and inferential analysis. Results: Out of the 10 SAS-SV questions answered by students, the mean points of each question totaled (p0.95)3.668∓ 0.159, with a standard deviation of 0.415 reflecting an answer leaning towards students slightly agreeing with the statements. Students seem to admit to exhibiting behaviours of hazardous phone use, even by their standards and judgement. The most common time of mobile phone use appears to be in the evening, where 83% of participants reported to mobile phone use at this time. The average LUHS student spends about 5 hours a day on their phone. When counting students' most preferred uses for mobile phones, we find that students report internet use as their most common reason for use, followed by messaging and listening to music. Conclusion: Students of LUHS appear to, on average, use their phones for at least 4 hours a day, peaking highest during the evening, for predominantly internet use, followed by listening to music and text messaging, with academic purposes being the last on the list. The consequences of increasing mobile phone use appear to be linked to dysfunction of daily activities by interfering with sleep as well as academic responsibilities, either causing or magnifying the stress experienced by students leading to lowered reported general well-being. Participants of LUHS reported being more likely to indulge in hazardous phone use than not, indicating that effects of sleep disturbance, daytime disturbance and reduced reported well-being may be prevalent among students of this institution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".